官网实例详解4.15（imdb_cnn_lstm.py）-keras学习笔记四

Keras实例目录

代码注释

'''Train a recurrent convolutional network on the IMDB sentiment
classification task.
为IMDB（数据集）情感分类任务训练循环卷积网络
Gets to 0.8498 test accuracy after 2 epochs. 41s/epoch on K520 GPU.
2个周期后0.8498测试准确率。用时41秒/周期（K520 GPU）.
'''
from __future__ import print_function

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import LSTM
from keras.layers import Conv1D, MaxPooling1D
from keras.datasets import imdb

# Embedding
# （词）嵌入
max_features = 20000
maxlen = 100
embedding_size = 128

# Convolution
# 卷积
kernel_size = 5
filters = 64
pool_size = 4

# LSTM
# 长短期记忆网络
lstm_output_size = 70

# Training
# 训练
batch_size = 30
epochs = 2

'''
Note:
batch_size is highly sensitive.
Only 2 epochs are needed as the dataset is very small.
'''

print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features) 
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')

print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)

print('Build model...')

model = Sequential()
model.add(Embedding(max_features, embedding_size, input_length=maxlen))
model.add(Dropout(0.25))
model.add(Conv1D(filters,
                 kernel_size,
                 padding='valid',
                 activation='relu',
                 strides=1))
model.add(MaxPooling1D(pool_size=pool_size))
model.add(LSTM(lstm_output_size))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

print('Train...')
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          validation_data=(x_test, y_test))
score, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)

代码执行

C:\ProgramData\Anaconda3\python.exe E:/keras-master/examples/imdb_cnn_lstm.py
Using TensorFlow backend.
Loading data...
25000 train sequences
25000 test sequences
Pad sequences (samples x time)
x_train shape: (25000, 100)
x_test shape: (25000, 100)
Build model...
Train...
Train on 25000 samples, validate on 25000 samples
Epoch 1/2

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25000/25000 [==============================] - 87s 3ms/step - loss: 0.3864 - acc: 0.8202 - val_loss: 0.3442 - val_acc: 0.8470
Epoch 2/2

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   60/25000 [..............................] - ETA: 1:04 - loss: 0.2241 - acc: 0.9167
 
24990/25000 [============================>.] - ETA: 0s
25000/25000 [==============================] - 15s 602us/step
Test score: 0.34116108905673026
Test accuracy: 0.8569999931097031

Process finished with exit code 0

C:\ProgramData\Anaconda3\python.exe E:/keras-master/examples/imdb_bidirectional_lstm.py
Using TensorFlow backend.
Loading data...
25000 train sequences
25000 test sequences
Pad sequences (samples x time)
x_train shape: (25000, 100)
x_test shape: (25000, 100)
Train...
Train on 25000 samples, validate on 25000 samples
Epoch 1/4

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 3744/25000 [===>..........................] - ETA: 4:21 - loss: 0.6195 - acc: 0.6637
 3776/25000 [===>..........................] - ETA: 4:20 - loss: 0.6178 - acc: 0.6650
 3808/25000 [===>..........................] - ETA: 4:19 - loss: 0.6172 - acc: 0.6657
 3840/25000 [===>..........................] - ETA: 4:19 - loss: 0.6157 - acc: 0.6674
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 3936/25000 [===>..........................] - ETA: 4:16 - loss: 0.6123 - acc: 0.6712
 3968/25000 [===>..........................] - ETA: 4:15 - loss: 0.6104 - acc: 0.6724
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 4160/25000 [===>..........................] - ETA: 4:13 - loss: 0.6033 - acc: 0.6774
 4192/25000 [====>.........................] - ETA: 4:12 - loss: 0.6015 - acc: 0.6787
 4224/25000 [====>.........................] - ETA: 4:12 - loss: 0.6010 - acc: 0.6790
 4256/25000 [====>.........................] - ETA: 4:11 - loss: 0.6005 - acc: 0.6793
 4288/25000 [====>.........................] - ETA: 4:10 - loss: 0.5993 - acc: 0.6800
 4320/25000 [====>.........................] - ETA: 4:10 - loss: 0.5979 - acc: 0.6810
 4352/25000 [====>.........................] - ETA: 4:10 - loss: 0.5978 - acc: 0.6811
 4384/25000 [====>.........................] - ETA: 4:09 - loss: 0.5969 - acc: 0.6818
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 4448/25000 [====>.........................] - ETA: 4:08 - loss: 0.5966 - acc: 0.6821
 4480/25000 [====>.........................] - ETA: 4:07 - loss: 0.5952 - acc: 0.6830
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 4576/25000 [====>.........................] - ETA: 4:06 - loss: 0.5944 - acc: 0.6844
 4608/25000 [====>.........................] - ETA: 4:05 - loss: 0.5935 - acc: 0.6858
 4640/25000 [====>.........................] - ETA: 4:04 - loss: 0.5927 - acc: 0.6858
 4672/25000 [====>.........................] - ETA: 4:04 - loss: 0.5916 - acc: 0.6864
 4704/25000 [====>.........................] - ETA: 4:03 - loss: 0.5902 - acc: 0.6873
 4736/25000 [====>.........................] - ETA: 4:02 - loss: 0.5892 - acc: 0.6879
 4768/25000 [====>.........................] - ETA: 4:02 - loss: 0.5896 - acc: 0.6877
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 4832/25000 [====>.........................] - ETA: 4:01 - loss: 0.5869 - acc: 0.6894
 4864/25000 [====>.........................] - ETA: 4:01 - loss: 0.5863 - acc: 0.6900
 4896/25000 [====>.........................] - ETA: 4:00 - loss: 0.5874 - acc: 0.6900
 4928/25000 [====>.........................] - ETA: 4:00 - loss: 0.5870 - acc: 0.6901
 4960/25000 [====>.........................] - ETA: 3:59 - loss: 0.5856 - acc: 0.6911
 4992/25000 [====>.........................] - ETA: 3:59 - loss: 0.5850 - acc: 0.6917
 5024/25000 [=====>........................] - ETA: 3:58 - loss: 0.5846 - acc: 0.6927
 5056/25000 [=====>........................] - ETA: 3:57 - loss: 0.5844 - acc: 0.6932
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 5248/25000 [=====>........................] - ETA: 3:54 - loss: 0.5776 - acc: 0.6991
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25000/25000 [==============================] - 334s 13ms/step - loss: 0.4116 - acc: 0.8104 - val_loss: 0.3410 - val_acc: 0.8484
Epoch 2/4

32/25000 [..............................] - ETA: 4:01 - loss: 0.1715 - acc: 0.9688

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25000/25000 [==============================] - 255s 10ms/step - loss: 0.0695 - acc: 0.9764 - val_loss: 0.5905 - val_acc: 0.8332

Process finished with exit code 0

Keras详细介绍

英文：https://keras.io/

中文：http://keras-cn.readthedocs.io/en/latest/

实例下载

https://github.com/keras-team/keras

https://github.com/keras-team/keras/tree/master/examples

完整项目下载

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包括：代码、数据集合（图片）、已生成model、安装库文件等。